1- importing libraries

import os
import warnings
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from pandas.api.types import CategoricalDtype
import plotly.express as px

from sklearn.feature_selection import mutual_info_regression



# Set Matplotlib defaults
plt.style.use("seaborn-whitegrid")
plt.rc("figure", autolayout=True)
plt.rc(
    "axes",
    labelweight="bold",
    labelsize="large",
    titleweight="bold",
    titlesize=14,
    titlepad=10,
)

# Mute warnings
warnings.filterwarnings('ignore')

2- Data preprocessing

data_dir = Path("Data and Submission")
df_train = pd.read_csv(data_dir / "train.csv", index_col="PassengerId")
df_test = pd.read_csv(data_dir / "test.csv", index_col="PassengerId")
df = pd.concat([df_train, df_test])

df.head()

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0.0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1.0	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1.0	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1.0	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0.0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

df.info()

## we have some null values in Age, Cabin, Embarked,Fare.
## will read the data description to try and figure the reason for that 

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 1 to 1309
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    float64
 1   Pclass    1309 non-null   int64  
 2   Name      1309 non-null   object 
 3   Sex       1309 non-null   object 
 4   Age       1046 non-null   float64
 5   SibSp     1309 non-null   int64  
 6   Parch     1309 non-null   int64  
 7   Ticket    1309 non-null   object 
 8   Fare      1308 non-null   float64
 9   Cabin     295 non-null    object 
 10  Embarked  1307 non-null   object 
dtypes: float64(3), int64(3), object(5)
memory usage: 122.7+ KB

df.Age.isnull().sum()
## we will impute missing values of Age with back/forward sampling

263

df.Cabin.isnull().sum()
##will drop this column as ist seems not relatable 

1014

print (df.Embarked.unique())
print(df.Embarked.isnull().sum())
##will impute it but will keep an eye for maybe dropping it as it seems not relatable 

['S' 'C' 'Q' nan]
2

checking for typos with the data descreption

cols = df.columns
for col in df.columns:
    print(col , df[col].unique())
    

Survived [ 0.  1. nan]
Pclass [3 1 2]
Name ['Braund, Mr. Owen Harris'
 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)'
 'Heikkinen, Miss. Laina' ... 'Saether, Mr. Simon Sivertsen'
 'Ware, Mr. Frederick' 'Peter, Master. Michael J']
Sex ['male' 'female']
Age [22.   38.   26.   35.     nan 54.    2.   27.   14.    4.   58.   20.
 39.   55.   31.   34.   15.   28.    8.   19.   40.   66.   42.   21.
 18.    3.    7.   49.   29.   65.   28.5   5.   11.   45.   17.   32.
 16.   25.    0.83 30.   33.   23.   24.   46.   59.   71.   37.   47.
 14.5  70.5  32.5  12.    9.   36.5  51.   55.5  40.5  44.    1.   61.
 56.   50.   36.   45.5  20.5  62.   41.   52.   63.   23.5   0.92 43.
 60.   10.   64.   13.   48.    0.75 53.   57.   80.   70.   24.5   6.
  0.67 30.5   0.42 34.5  74.   22.5  18.5  67.   76.   26.5  60.5  11.5
  0.33  0.17 38.5 ]
SibSp [1 0 3 4 2 5 8]
Parch [0 1 2 5 3 4 6 9]
Ticket ['A/5 21171' 'PC 17599' 'STON/O2. 3101282' '113803' '373450' '330877'
 '17463' '349909' '347742' '237736' 'PP 9549' '113783' 'A/5. 2151'
 '347082' '350406' '248706' '382652' '244373' '345763' '2649' '239865'
 '248698' '330923' '113788' '347077' '2631' '19950' '330959' '349216'
 'PC 17601' 'PC 17569' '335677' 'C.A. 24579' 'PC 17604' '113789' '2677'
 'A./5. 2152' '345764' '2651' '7546' '11668' '349253' 'SC/Paris 2123'
 '330958' 'S.C./A.4. 23567' '370371' '14311' '2662' '349237' '3101295'
 'A/4. 39886' 'PC 17572' '2926' '113509' '19947' 'C.A. 31026' '2697'
 'C.A. 34651' 'CA 2144' '2669' '113572' '36973' '347088' 'PC 17605' '2661'
 'C.A. 29395' 'S.P. 3464' '3101281' '315151' 'C.A. 33111' 'S.O.C. 14879'
 '2680' '1601' '348123' '349208' '374746' '248738' '364516' '345767'
 '345779' '330932' '113059' 'SO/C 14885' '3101278' 'W./C. 6608'
 'SOTON/OQ 392086' '343275' '343276' '347466' 'W.E.P. 5734' 'C.A. 2315'
 '364500' '374910' 'PC 17754' 'PC 17759' '231919' '244367' '349245'
 '349215' '35281' '7540' '3101276' '349207' '343120' '312991' '349249'
 '371110' '110465' '2665' '324669' '4136' '2627' 'STON/O 2. 3101294'
 '370369' 'PC 17558' 'A4. 54510' '27267' '370372' 'C 17369' '2668'
 '347061' '349241' 'SOTON/O.Q. 3101307' 'A/5. 3337' '228414' 'C.A. 29178'
 'SC/PARIS 2133' '11752' '7534' 'PC 17593' '2678' '347081'
 'STON/O2. 3101279' '365222' '231945' 'C.A. 33112' '350043' '230080'
 '244310' 'S.O.P. 1166' '113776' 'A.5. 11206' 'A/5. 851' 'Fa 265302'
 'PC 17597' '35851' 'SOTON/OQ 392090' '315037' 'CA. 2343' '371362'
 'C.A. 33595' '347068' '315093' '363291' '113505' 'PC 17318' '111240'
 'STON/O 2. 3101280' '17764' '350404' '4133' 'PC 17595' '250653' 'LINE'
 'SC/PARIS 2131' '230136' '315153' '113767' '370365' '111428' '364849'
 '349247' '234604' '28424' '350046' 'PC 17610' '368703' '4579' '370370'
 '248747' '345770' '3101264' '2628' 'A/5 3540' '347054' '2699' '367231'
 '112277' 'SOTON/O.Q. 3101311' 'F.C.C. 13528' 'A/5 21174' '250646'
 '367229' '35273' 'STON/O2. 3101283' '243847' '11813' 'W/C 14208'
 'SOTON/OQ 392089' '220367' '21440' '349234' '19943' 'PP 4348' 'SW/PP 751'
 'A/5 21173' '236171' '347067' '237442' 'C.A. 29566' 'W./C. 6609' '26707'
 'C.A. 31921' '28665' 'SCO/W 1585' '367230' 'W./C. 14263'
 'STON/O 2. 3101275' '2694' '19928' '347071' '250649' '11751' '244252'
 '362316' '113514' 'A/5. 3336' '370129' '2650' 'PC 17585' '110152'
 'PC 17755' '230433' '384461' '110413' '112059' '382649' 'C.A. 17248'
 '347083' 'PC 17582' 'PC 17760' '113798' '250644' 'PC 17596' '370375'
 '13502' '347073' '239853' 'C.A. 2673' '336439' '347464' '345778'
 'A/5. 10482' '113056' '349239' '345774' '349206' '237798' '370373'
 '19877' '11967' 'SC/Paris 2163' '349236' '349233' 'PC 17612' '2693'
 '113781' '19988' '9234' '367226' '226593' 'A/5 2466' '17421' 'PC 17758'
 'P/PP 3381' 'PC 17485' '11767' 'PC 17608' '250651' '349243'
 'F.C.C. 13529' '347470' '29011' '36928' '16966' 'A/5 21172' '349219'
 '234818' '345364' '28551' '111361' '113043' 'PC 17611' '349225' '7598'
 '113784' '248740' '244361' '229236' '248733' '31418' '386525'
 'C.A. 37671' '315088' '7267' '113510' '2695' '2647' '345783' '237671'
 '330931' '330980' 'SC/PARIS 2167' '2691' 'SOTON/O.Q. 3101310' 'C 7076'
 '110813' '2626' '14313' 'PC 17477' '11765' '3101267' '323951' 'C 7077'
 '113503' '2648' '347069' 'PC 17757' '2653' 'STON/O 2. 3101293' '349227'
 '27849' '367655' 'SC 1748' '113760' '350034' '3101277' '350052' '350407'
 '28403' '244278' '240929' 'STON/O 2. 3101289' '341826' '4137' '315096'
 '28664' '347064' '29106' '312992' '349222' '394140' 'STON/O 2. 3101269'
 '343095' '28220' '250652' '28228' '345773' '349254' 'A/5. 13032' '315082'
 '347080' 'A/4. 34244' '2003' '250655' '364851' 'SOTON/O.Q. 392078'
 '110564' '376564' 'SC/AH 3085' 'STON/O 2. 3101274' '13507' 'C.A. 18723'
 '345769' '347076' '230434' '65306' '33638' '113794' '2666' '113786'
 '65303' '113051' '17453' 'A/5 2817' '349240' '13509' '17464'
 'F.C.C. 13531' '371060' '19952' '364506' '111320' '234360' 'A/S 2816'
 'SOTON/O.Q. 3101306' '113792' '36209' '323592' '315089' 'SC/AH Basle 541'
 '7553' '31027' '3460' '350060' '3101298' '239854' 'A/5 3594' '4134'
 '11771' 'A.5. 18509' '65304' 'SOTON/OQ 3101317' '113787' 'PC 17609'
 'A/4 45380' '36947' 'C.A. 6212' '350035' '315086' '364846' '330909'
 '4135' '26360' '111427' 'C 4001' '382651' 'SOTON/OQ 3101316' 'PC 17473'
 'PC 17603' '349209' '36967' 'C.A. 34260' '226875' '349242' '12749'
 '349252' '2624' '2700' '367232' 'W./C. 14258' 'PC 17483' '3101296'
 '29104' '2641' '2690' '315084' '113050' 'PC 17761' '364498' '13568'
 'WE/P 5735' '2908' '693' 'SC/PARIS 2146' '244358' '330979' '2620'
 '347085' '113807' '11755' '345572' '372622' '349251' '218629'
 'SOTON/OQ 392082' 'SOTON/O.Q. 392087' 'A/4 48871' '349205' '2686'
 '350417' 'S.W./PP 752' '11769' 'PC 17474' '14312' 'A/4. 20589' '358585'
 '243880' '2689' 'STON/O 2. 3101286' '237789' '13049' '3411' '237565'
 '13567' '14973' 'A./5. 3235' 'STON/O 2. 3101273' 'A/5 3902' '364848'
 'SC/AH 29037' '248727' '2664' '349214' '113796' '364511' '111426'
 '349910' '349246' '113804' 'SOTON/O.Q. 3101305' '370377' '364512'
 '220845' '31028' '2659' '11753' '350029' '54636' '36963' '219533'
 '349224' '334912' '27042' '347743' '13214' '112052' '237668'
 'STON/O 2. 3101292' '350050' '349231' '13213' 'S.O./P.P. 751' 'CA. 2314'
 '349221' '8475' '330919' '365226' '349223' '29751' '2623' '5727' '349210'
 'STON/O 2. 3101285' '234686' '312993' 'A/5 3536' '19996' '29750'
 'F.C. 12750' 'C.A. 24580' '244270' '239856' '349912' '342826' '4138'
 '330935' '6563' '349228' '350036' '24160' '17474' '349256' '2672'
 '113800' '248731' '363592' '35852' '348121' 'PC 17475' '36864' '350025'
 '223596' 'PC 17476' 'PC 17482' '113028' '7545' '250647' '348124' '34218'
 '36568' '347062' '350048' '12233' '250643' '113806' '315094' '36866'
 '236853' 'STON/O2. 3101271' '239855' '28425' '233639' '349201' '349218'
 '16988' '376566' 'STON/O 2. 3101288' '250648' '113773' '335097' '29103'
 '392096' '345780' '349204' '350042' '29108' '363294' 'SOTON/O2 3101272'
 '2663' '347074' '112379' '364850' '8471' '345781' '350047' 'S.O./P.P. 3'
 '2674' '29105' '347078' '383121' '36865' '2687' '113501' 'W./C. 6607'
 'SOTON/O.Q. 3101312' '374887' '3101265' '12460' 'PC 17600' '349203'
 '28213' '17465' '349244' '2685' '2625' '347089' '347063' '112050'
 '347087' '248723' '3474' '28206' '364499' '112058' 'STON/O2. 3101290'
 'S.C./PARIS 2079' 'C 7075' '315098' '19972' '368323' '367228' '2671'
 '347468' '2223' 'PC 17756' '315097' '392092' '11774' 'SOTON/O2 3101287'
 '2683' '315090' 'C.A. 5547' '349213' '347060' 'PC 17592' '392091'
 '113055' '2629' '350026' '28134' '17466' '233866' '236852'
 'SC/PARIS 2149' 'PC 17590' '345777' '349248' '695' '345765' '2667'
 '349212' '349217' '349257' '7552' 'C.A./SOTON 34068' 'SOTON/OQ 392076'
 '211536' '112053' '111369' '370376' '330911' '363272' '240276' '315154'
 '7538' '330972' '2657' '349220' '694' '21228' '24065' '233734' '2692'
 'STON/O2. 3101270' '2696' 'C 17368' 'PC 17598' '2698' '113054'
 'C.A. 31029' '13236' '2682' '342712' '315087' '345768' '113778'
 'SOTON/O.Q. 3101263' '237249' 'STON/O 2. 3101291' 'PC 17594' '370374'
 '13695' 'SC/PARIS 2168' 'SC/A.3 2861' '349230' '348122' '349232' '237216'
 '347090' '334914' 'F.C.C. 13534' '330963' '2543' '382653' '349211'
 '3101297' 'PC 17562' '359306' '11770' '248744' '368702' '19924' '349238'
 '240261' '2660' '330844' 'A/4 31416' '364856' '347072' '345498' '376563'
 '13905' '350033' 'STON/O 2. 3101268' '347471' 'A./5. 3338' '11778'
 '365235' '347070' '330920' '383162' '3410' '248734' '237734' '330968'
 'PC 17531' '329944' '2681' '13050' '367227' '392095' '368783' '350045'
 '211535' '342441' 'STON/OQ. 369943' '113780' '2621' '349226' '350409'
 '2656' '248659' 'SOTON/OQ 392083' '17475' 'SC/A4 23568' '113791' '349255'
 '3701' '350405' 'S.O./P.P. 752' '347469' '110489' 'SOTON/O.Q. 3101315'
 '335432' '220844' '343271' '237393' 'PC 17591' '17770' '7548'
 'S.O./P.P. 251' '2670' '2673' '233478' '7935' '239059' 'S.O./P.P. 2'
 'A/4 48873' '28221' '111163' '235509' '347465' '347066' 'C.A. 31030'
 '65305' 'C.A. 34050' 'F.C. 12998' '9232' '28034' 'PC 17613' '349250'
 'SOTON/O.Q. 3101308' '347091' '113038' '330924' '32302' 'SC/PARIS 2148'
 '342684' 'W./C. 14266' '350053' 'PC 17606' '350054' '370368' '242963'
 '113795' '3101266' '330971' '350416' '2679' '250650' '112377' '3470'
 'SOTON/O2 3101284' '13508' '7266' '345775' 'C.A. 42795' 'AQ/4 3130'
 '363611' '28404' '345501' '350410' 'C.A. 34644' '349235' '112051'
 'C.A. 49867' 'A. 2. 39186' '315095' '368573' '2676' 'SC 14888' 'CA 31352'
 'W./C. 14260' '315085' '364859' 'A/5 21175' 'SOTON/O.Q. 3101314' '2655'
 'A/5 1478' 'PC 17607' '382650' '2652' '345771' '349202' '113801' '347467'
 '347079' '237735' '315092' '383123' '112901' '315091' '2658' 'LP 1588'
 '368364' 'AQ/3. 30631' '28004' '350408' '347075' '2654' '244368' '113790'
 'SOTON/O.Q. 3101309' '236854' 'PC 17580' '2684' '349229' '110469'
 '244360' '2675' '2622' 'C.A. 15185' '350403' '348125' '237670' '2688'
 '248726' 'F.C.C. 13540' '113044' '1222' '368402' '315083' '112378'
 'SC/PARIS 2147' '28133' '248746' '315152' '29107' '680' '366713' '330910'
 'SC/PARIS 2159' '349911' '244346' '364858' 'C.A. 30769' '371109' '347065'
 '21332' '17765' 'SC/PARIS 2166' '28666' '334915' '365237' '347086'
 'A.5. 3236' 'SOTON/O.Q. 3101262' '359309']
Fare [  7.25    71.2833   7.925   53.1      8.05     8.4583  51.8625  21.075
  11.1333  30.0708  16.7     26.55    31.275    7.8542  16.      29.125
  13.      18.       7.225   26.       8.0292  35.5     31.3875 263.
   7.8792   7.8958  27.7208 146.5208   7.75    10.5     82.1708  52.
   7.2292  11.2417   9.475   21.      41.5792  15.5     21.6792  17.8
  39.6875   7.8     76.7292  61.9792  27.75    46.9     80.      83.475
  27.9     15.2458   8.1583   8.6625  73.5     14.4542  56.4958   7.65
  29.      12.475    9.       9.5      7.7875  47.1     15.85    34.375
  61.175   20.575   34.6542  63.3583  23.      77.2875   8.6542   7.775
  24.15     9.825   14.4583 247.5208   7.1417  22.3583   6.975    7.05
  14.5     15.0458  26.2833   9.2167  79.2      6.75    11.5     36.75
   7.7958  12.525   66.6      7.3125  61.3792   7.7333  69.55    16.1
  15.75    20.525   55.      25.925   33.5     30.6958  25.4667  28.7125
   0.      15.05    39.      22.025   50.       8.4042   6.4958  10.4625
  18.7875  31.     113.275   27.      76.2917  90.       9.35    13.5
   7.55    26.25    12.275    7.125   52.5542  20.2125  86.5    512.3292
  79.65   153.4625 135.6333  19.5     29.7     77.9583  20.25    78.85
  91.0792  12.875    8.85   151.55    30.5     23.25    12.35   110.8833
 108.9     24.      56.9292  83.1583 262.375   14.     164.8667 134.5
   6.2375  57.9792  28.5    133.65    15.9      9.225   35.      75.25
  69.3     55.4417 211.5      4.0125 227.525   15.7417   7.7292  12.
 120.      12.65    18.75     6.8583  32.5      7.875   14.4     55.9
   8.1125  81.8583  19.2583  19.9667  89.1042  38.5      7.725   13.7917
   9.8375   7.0458   7.5208  12.2875   9.5875  49.5042  78.2667  15.1
   7.6292  22.525   26.2875  59.4      7.4958  34.0208  93.5    221.7792
 106.425   49.5     71.      13.8625   7.8292  39.6     17.4     51.4792
  26.3875  30.      40.125    8.7125  15.      33.      42.4     15.55
  65.      32.3208   7.0542   8.4333  25.5875   9.8417   8.1375  10.1708
 211.3375  57.      13.4167   7.7417   9.4833   7.7375   8.3625  23.45
  25.9292   8.6833   8.5167   7.8875  37.0042   6.45     6.95     8.3
   6.4375  39.4     14.1083  13.8583  50.4958   5.       9.8458  10.5167
   7.       9.6875  82.2667   3.1708  31.6833  31.5     57.75     7.85
  60.      15.0333  15.5792  28.5375  25.7     10.7083  13.9      7.8208
   7.7792  31.6792   7.2833  75.2417      nan  12.1833  13.775    8.9625
  25.7417  42.5     27.4458 136.7792   9.325   12.7375  45.5      7.575
   7.5792   7.7208]
Cabin [nan 'C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'B78' 'D33'
 'B30' 'C52' 'B28' 'C83' 'F33' 'F G73' 'E31' 'A5' 'D10 D12' 'D26' 'C110'
 'B58 B60' 'E101' 'F E69' 'D47' 'B86' 'F2' 'C2' 'E33' 'B19' 'A7' 'C49'
 'F4' 'A32' 'B4' 'B80' 'A31' 'D36' 'D15' 'C93' 'C78' 'D35' 'C87' 'B77'
 'E67' 'B94' 'C125' 'C99' 'C118' 'D7' 'A19' 'B49' 'D' 'C22 C26' 'C106'
 'C65' 'E36' 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'B18' 'C124' 'C91'
 'E40' 'T' 'C128' 'D37' 'B35' 'E50' 'C82' 'B96 B98' 'E10' 'E44' 'A34'
 'C104' 'C111' 'C92' 'E38' 'D21' 'E12' 'E63' 'A14' 'B37' 'C30' 'D20' 'B79'
 'E25' 'D46' 'B73' 'C95' 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'C101' 'C68'
 'A10' 'E68' 'B41' 'A20' 'D19' 'D50' 'D9' 'A23' 'B50' 'A26' 'D48' 'E58'
 'C126' 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'F G63' 'C62 C64' 'E24' 'C90'
 'C45' 'E8' 'B101' 'D45' 'C46' 'D30' 'E121' 'D11' 'E77' 'F38' 'B3' 'D6'
 'B82 B84' 'D17' 'A36' 'B102' 'B69' 'E49' 'C47' 'D28' 'E17' 'A24' 'C50'
 'B42' 'C148' 'B45' 'B36' 'A21' 'D34' 'A9' 'C31' 'B61' 'C53' 'D43' 'C130'
 'C132' 'C55 C57' 'C116' 'F' 'A29' 'C6' 'C28' 'C51' 'C97' 'D22' 'B10'
 'E45' 'E52' 'A11' 'B11' 'C80' 'C89' 'F E46' 'B26' 'F E57' 'A18' 'E60'
 'E39 E41' 'B52 B54 B56' 'C39' 'B24' 'D40' 'D38' 'C105']
Embarked ['S' 'C' 'Q' nan]

##doesnt seem like we have typos 

clean function

def clean(df):
    #df = df.drop('Name', axis =1)
    #df = df.drop('Cabin', axis =1)
    #df = df.drop('Ticket', axis =1)
    df['Age']= df['Age'].fillna(method = 'ffill')
    df['Embarked'] = df['Embarked'].fillna(method = 'ffill')
    #df['Age_'] = pd.cut(df["Age"], bins = [0,15,40,100], labels = [0,1,2])
    #df = df.drop('Age', axis =1)
    df['Fare']= df['Fare'].fillna(method = 'ffill')
    df['Cabin']= df['Cabin'].fillna('None')

    return df

df=clean(df)
df_train = df.loc[df_train.index, :]
df_test = df.loc[df_test.index, :]

Data visualization

px.imshow(df_train.corr(),title="Correlation Plot of the Heat Failure Prediction")

df_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    float64
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       891 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     891 non-null    object 
 10  Embarked  891 non-null    object 
dtypes: float64(3), int64(3), object(5)
memory usage: 83.5+ KB

for col in df_train.columns:
    fig=px.histogram(df_train, 
                 x="Survived",
                 color=col,
                 title="Distribution of survivals vs " + col,
                 barmode="group")
    fig.show()

encoding

df_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    float64
 1   Pclass    891 non-null    int64  
 2   Name      891 non-null    object 
 3   Sex       891 non-null    object 
 4   Age       891 non-null    float64
 5   SibSp     891 non-null    int64  
 6   Parch     891 non-null    int64  
 7   Ticket    891 non-null    object 
 8   Fare      891 non-null    float64
 9   Cabin     891 non-null    object 
 10  Embarked  891 non-null    object 
dtypes: float64(3), int64(3), object(5)
memory usage: 83.5+ KB

features_nom = ["Survived","Sex", "Embarked","Pclass","Cabin"]
#categories
for name in features_nom:
    df[name] = df[name].astype("category")

Feature Eng.

names_splits = df.Name.str.split(pat = ",",n=1, expand=True)

names_splits = names_splits[1].str.split(pat = "." , n=1, expand=True)
#names_splits = names_splits[1].str.split(pat = "" , n=1, expand=True)

df['Jobs'] = names_splits.iloc[:,0]

df['Jobs'].unique()

array([' Mr', ' Mrs', ' Miss', ' Master', ' Don', ' Rev', ' Dr', ' Mme',
       ' Ms', ' Major', ' Lady', ' Sir', ' Mlle', ' Col', ' Capt',
       ' the Countess', ' Jonkheer', ' Dona'], dtype=object)

# df['Titles'] = df['Jobs'].apply(lambda x:'Army' if x in [' Rev' , ' Major'  , ' Col' ,' Capt' ] else x)
# df['Titles'] = df['Titles'].apply(lambda x:'Royal' if x in [' Don' , ' Lady'  , ' Sir' ,' the Countess' , ' Jonkheer', ' Dona' ] else x)
# df['Titles'] = df['Titles'].apply(lambda x:'Normal' if x in [' Mr' , ' Mrs',' Miss',' Master',' Dr',' Mme',' Ms',' Mlle','' ] else x)

# df['Titles'].unique()

df['Jobs'].unique()

array([' Mr', ' Mrs', ' Miss', ' Master', ' Don', ' Rev', ' Dr', ' Mme',
       ' Ms', ' Major', ' Lady', ' Sir', ' Mlle', ' Col', ' Capt',
       ' the Countess', ' Jonkheer', ' Dona'], dtype=object)

# fig=px.histogram(df, 
#              x="Survived",
#              color='Titles',
#              title="Distribution of survivals vs Titles",
#              barmode="group")
# fig.show()

age_Group = pd.cut(df["Age"], bins = [0,6,18,35,65,80], labels = ["childs","Kids","adults","MiddleAged","Old"])
df["Age_Bins"] = age_Group

fig=px.histogram(df, 
             x="Survived",
             color='Age_Bins',
             title="Distribution of survivals vs AgeBins",
             barmode="group")
fig.show()

df["FamTotal"] = df["SibSp"] + df["Parch"]

fig=px.histogram(df, 
             x="Survived",
             color='FamTotal',
             title="Distribution of survivals vs FamTotal",
             barmode="group")
fig.show()

FamilyBins = pd.cut(df["FamTotal"], bins = [-1,1,4,7,10], labels = ["NoFam","few","mediamfam",'Bigfam'])
df["FamilyBins"] = FamilyBins

fig=px.histogram(df, 
             x="Survived",
             color='FamilyBins',
             title="Distribution of survivals vs FamilyBins",
             barmode="group")
fig.show()

df['AlphaTicket'] = df["Ticket"].apply(lambda x:1 if (x.islower()) or (x.isupper()) else 0)

fig=px.histogram(df, 
             x="Survived",
             color='AlphaTicket',
             title="Distribution of survivals vs AlphaTicket",
             barmode="group")
fig.show()

plt.figure(figsize=(15,10))
plt.title(f"Distribution of Fare Data")
sns.histplot(df['Fare'],kde=True)
plt.tight_layout()
plt.plot()
    

[]

FareBins = pd.cut(df["Fare"], bins = [-1,50,100,265,1000], labels = ["poor","normal","rich",'veryrich'])
df["FareBins"] = FareBins

fig=px.histogram(df, 
             x="Survived",
             color='FareBins',
             title="Distribution of survivals vs FareBins",
             barmode="group")
fig.show()

df['CabinLet'] = df.Cabin.str.split(pat = '0|1|2|3|4|5|6|7|8|9',n=1, expand=True )[0]
df['CabinLet'] = df['CabinLet'].apply(lambda x: x if len(x)< 1 else x[0])

fig=px.histogram(df, 
             x="Survived",
             color='CabinLet',
             title="Distribution of survivals vs CabinLet",
             barmode="group")
fig.show()

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 1 to 1309
Data columns (total 18 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   Survived     891 non-null    category
 1   Pclass       1309 non-null   category
 2   Name         1309 non-null   object  
 3   Sex          1309 non-null   category
 4   Age          1309 non-null   float64 
 5   SibSp        1309 non-null   int64   
 6   Parch        1309 non-null   int64   
 7   Ticket       1309 non-null   object  
 8   Fare         1309 non-null   float64 
 9   Cabin        1309 non-null   category
 10  Embarked     1309 non-null   category
 11  Jobs         1309 non-null   object  
 12  Age_Bins     1309 non-null   category
 13  FamTotal     1309 non-null   int64   
 14  FamilyBins   1309 non-null   category
 15  AlphaTicket  1309 non-null   int64   
 16  FareBins     1309 non-null   category
 17  CabinLet     1309 non-null   object  
dtypes: category(8), float64(2), int64(4), object(4)
memory usage: 162.9+ KB

df = df.drop('Name', axis=1)
df = df.drop('Ticket', axis=1)
df= df.drop('Cabin', axis=1)
#df= df.drop('Jobs', axis=1)
features_nom = ["Jobs","AlphaTicket", "CabinLet"]
#categories
for name in features_nom:
    df[name] = df[name].astype("category")

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 1 to 1309
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   Survived     891 non-null    category
 1   Pclass       1309 non-null   category
 2   Sex          1309 non-null   category
 3   Age          1309 non-null   float64 
 4   SibSp        1309 non-null   int64   
 5   Parch        1309 non-null   int64   
 6   Fare         1309 non-null   float64 
 7   Embarked     1309 non-null   category
 8   Jobs         1309 non-null   category
 9   Age_Bins     1309 non-null   category
 10  FamTotal     1309 non-null   int64   
 11  FamilyBins   1309 non-null   category
 12  AlphaTicket  1309 non-null   category
 13  FareBins     1309 non-null   category
 14  CabinLet     1309 non-null   category
dtypes: category(10), float64(2), int64(3)
memory usage: 108.7 KB

Y = df_train.pop("Survived")

df = df.drop('Survived', axis=1)

df = pd.get_dummies(df)

df_train = df.loc[df_train.index, :]
df_test = df.loc[df_test.index, :]

mi_scores = mutual_info_regression(df_train, Y, random_state=0)
mi_scores = pd.Series(mi_scores, name="MI Scores", index=df_train.columns)
mi_scores = mi_scores.sort_values(ascending=False)
mi_scores

Sex_male                0.222795
Jobs_ Mr                0.207942
Sex_female              0.135846
Fare                    0.127942
Jobs_ Mrs               0.071473
Pclass_3                0.054797
Pclass_2                0.047015
FamilyBins_NoFam        0.042736
Pclass_1                0.040613
CabinLet_N              0.039263
FareBins_poor           0.035934
CabinLet_E              0.033449
SibSp                   0.033286
Embarked_C              0.032546
CabinLet_B              0.027854
FareBins_normal         0.022931
Age                     0.022358
Age_Bins_Kids           0.020760
FareBins_veryrich       0.020495
FamilyBins_few          0.019499
FamTotal                0.017400
Jobs_ Don               0.017348
FamilyBins_Bigfam       0.015997
CabinLet_C              0.014716
AlphaTicket_0           0.010251
Jobs_ Rev               0.009886
Jobs_ Miss              0.008905
CabinLet_G              0.005904
CabinLet_F              0.004515
Age_Bins_MiddleAged     0.004006
FamilyBins_mediamfam    0.003912
Embarked_Q              0.001166
Jobs_ Capt              0.001141
AlphaTicket_1           0.000000
FareBins_rich           0.000000
CabinLet_A              0.000000
CabinLet_D              0.000000
Jobs_ Ms                0.000000
Age_Bins_Old            0.000000
Jobs_ Lady              0.000000
Parch                   0.000000
Embarked_S              0.000000
Jobs_ Col               0.000000
Jobs_ Dona              0.000000
Jobs_ Dr                0.000000
Jobs_ Jonkheer          0.000000
Jobs_ Major             0.000000
Age_Bins_adults         0.000000
Jobs_ Master            0.000000
Jobs_ Mlle              0.000000
Jobs_ Mme               0.000000
Jobs_ Sir               0.000000
Jobs_ the Countess      0.000000
Age_Bins_childs         0.000000
CabinLet_T              0.000000
Name: MI Scores, dtype: float64

# from sklearn.model_selection import train_test_split
# X_train, X_valid, y_train, y_valid = train_test_split(
#                 df_train, Y, test_size=0.20, random_state=42)

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

for i in range (80 , 400 , 10):
    clf = RandomForestClassifier(n_estimators=i , max_depth=6,
                                 random_state=0,min_samples_split=3,min_samples_leaf=1)

    scores = cross_val_score(clf, df_train, Y, cv=5)
    plt.scatter(i , scores.mean() )

clf = RandomForestClassifier(n_estimators=380 , max_depth=6,
                                 random_state=0,min_samples_split=3
                                 ,min_samples_leaf=1)
clf.fit(df_train, Y)
#clf = RandomForestClassifier(n_estimators=140 , max_depth=6, random_state=42) best yet

RandomForestClassifier(max_depth=6, min_samples_split=3, n_estimators=380,
                       random_state=0)

preds = clf.predict(df_test).astype('int')

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
clf = LogisticRegression(random_state=0,max_iter=400)
scores = cross_val_score(clf, df_train, Y, cv=5)
scores.mean()

0.817079907099366

output = pd.DataFrame({'PassengerId': df_test.index, 'Survived': preds})
output.to_csv('my_submission.csv', index=False)
print("Your submission was successfully saved!")

Your submission was successfully saved!